Safe driving monitoring system

ABSTRACT

A method and system for detecting unsafe or suspect activities such as distracted driving associates distracted driving events to the road type, vehicle speed and vehicle acceleration (positive, negative and lateral) at the time of the distracted driving event, and identifies severe distracted driving events from a large population of minor events using a statistical distribution such as a Cauchy distribution equation. The system employs a smartphone application (App) coupled with a central server that computes driver safety scores which relate time of day, road type, vehicle speed, vehicle acceleration (positive, negative and lateral) and distracted driving using the Cauchy distribution equation. The server renders summary and detail reports of driving scores and distracted driving events to concerned parties including insurance companies, fleet managers, vehicle owners and the parents/guardians of teenaged drivers.

RELATED APPLICATIONS

This patent application is a continuation-in-part (CIP) under 35 U.S.C.§120 of U.S. patent application Ser. No. 11/370,651, Filed Mar. 8, 2006,entitled “METHOD AND APPARATUS FOR DETERMINING AND STORING EXCESSIVEVEHICLE SPEED,” and claims the benefit under 35 U.S.C. §119(e) of U.S.Provisional Patent App. No. 61/613,690, filed Mar. 21, 2012, entitled“MONITORING AND REPORTING OF DISTRACTED DRIVING EVENTS,” bothincorporated herein by reference in entirety.

BACKGROUND

Modern proliferation of smaller personal electronic devices, as well asincreasing bandwidth and connectivity, allows usage and enjoyment ofsuch devices in an increasing number of locations. Combined withseemingly endless availability of consumer commodities such as food andbeverage offerings available for take-out and drive-thru, contributes toa plethora of potential driving distractions, particularly for novicedrivers. Media attention has also heightened as an increasing number ofdriving mishaps are traced to distracted driving, often resulting fromusage of personal electronic devices.

Distracted driving includes any activity that could divert a person'sattention away from the primary task of driving. All distractionsendanger driver, passenger, and bystander safety. In particular,however, because text messaging, email usage and web browsing requirevisual, manual, and cognitive attention from the driver, they are by farthe most alarming distractions. This problem exists for all types ofdriver: professional fleet drivers, non-commercial drivers who use acompany vehicle for business, drivers of their own private vehicles,inexperienced drivers and teenaged drivers.

SUMMARY

A method and system for detecting unsafe or suspect activities such asdistracted driving associates distracted driving events to the roadtype, vehicle speed and vehicle acceleration (positive, negative andlateral) at the time of the distracted driving event, and identifiessevere distracted driving events from a large population of minor eventsusing a statistical distribution such as a Cauchy distribution equation.The system employs a smartphone application (App) coupled with a centralserver that computes driver safety scores which relate time of day, roadtype, vehicle speed, vehicle acceleration (positive, negative andlateral) and distracted driving using the Cauchy distribution equation.The server renders summary and detail reports of driving scores anddistracted driving events to concerned parties including insurancecompanies, fleet managers, vehicle owners and the parents/guardians ofteenaged drivers.

Configurations herein are based, in part, on the observation thatconventional approaches to driving assessment focus on individual eventsand preventing or tracking such specific events, such as preventing useof certain apps (i.e. texting) or exceeding a pre-set speed limit (i.e.85 mph). Unfortunately, such conventional approaches fail to consider anoverall performance of a monitored driver that considers positive andnegative factors over an extended time period to assess a level of care.Accordingly, configurations herein substantially overcome the abovedescribed shortcomings by determining an aggregate score of a drivingsession by accumulating a number of factors related to distracted,inattentive, and/or risky driving behavior, and computing a conclusivesummation of results, rather than an unrelated set of specific events. Adriving score includes measurement of abrupt actions (starting,stopping), sudden movements (angular velocity indicative of hardcornering), vehicle speed over a range of different road types, andusage of particular applications (apps) on a personal device.

Personal electronic devices such cellphones, smartphones, tablets andthe like are often carried as commonly as keys, wallet or purse. Thedriving monitoring system as disclosed herein takes the form of adownloadable app which launches and executes on the personal device andcommunicates with a central server for reporting driving activity,particularly suspect activities which may indicate a potentially riskyor dangerous behavior. In particular, the app monitors the use of otherapps which may be considered to distract the driver from attention tothe road, and motion parameters such as speed and angular velocity whichindicate physical driving patterns considered to be associated withinattentive, risky, or distracted behavior.

Events gathered by the app are communicated to a server for coalescingwith other raw event data of other drivers, and computing reports ofdriving habits as well as a score which is normalized with scores ofother drivers to provide an objective assessment of driving. Astatistical distribution such as a Cauchy distribution measures aseverity of an event, and may emphasize a significant deviation as aseparate notification. For example, a 5-10 mph (miles per hour) speedlimit transgression is not as serious as a 90 mph traveling speed.Reports and individual events are reported to a supervisory or ownershipentity such as a parent or employer. Further, verification of appoperation is verified so that a driver may not avoid scrutiny bydisabling the app or powering down the personal device.

In an example configuration discussed below, the disclosed method fortracking driving habits includes associating a personal device with adriver in a vehicle, determining that the driver is engaging in asuspect activity while driving, comparing the suspect activity with aseverity scale indicative of a relative risk of the activity, computinga score based on the comparison and reporting the determined suspectactivity and the score to a repository for accumulating a drivinghistory of the driver. The method may include more specific operationsdepending on the type of suspect event. For example, the method fortracking driving events may include determining, via a personal deviceassociated with a driver in a vehicle, a speed of the vehicle based onrelative positioning of the personal device, and receiving a userdefined speed threshold corresponding to a road type determined from theposition of the vehicle, in which the threshold is defined for the roadtype. The method compares the speed to the received threshold andreports the determined speed and the road type if the determined speedexceeds the threshold corresponding to the road type currently beingtraveled.

An environment for operation of the system and method disclosed hereinincludes a server for aggregating and reporting distracted drivingevents comprising, a repository for storing suspect events received frompersonal devices corresponding to a plurality of monitored drivers, anaccess connection from the server to a personal device of each of theplurality of monitored drivers, and scoring logic for receiving thesuspect events from the personal devices and computing a scoreindicative of safe driving habits exhibited by the monitored drivers.

Alternate configurations of the invention include a multiprogramming ormultiprocessing computerized device such as a multiprocessor, controlleror dedicated computing device or the like configured with softwareand/or circuitry (e.g., a processor as summarized above) to process anyor all of the method operations disclosed herein as embodiments of theinvention. Still other embodiments of the invention include softwareprograms such as a Java Virtual Machine and/or an operating system thatcan operate alone or in conjunction with each other with amultiprocessing computerized device to perform the method embodimentsteps and operations summarized above and disclosed in detail below. Onesuch embodiment comprises a computer program product that has anon-transitory computer-readable storage medium including computerprogram logic encoded as instructions thereon that, when performed in amultiprocessing computerized device having a coupling of a memory and aprocessor, programs the processor to perform the operations disclosedherein as embodiments of the invention to carry out data accessrequests. Such arrangements of the invention are typically provided assoftware, code and/or other data (e.g., data structures) arranged orencoded on a computer readable medium such as an optical medium (e.g.,CD-ROM), floppy or hard disk or other medium such as firmware ormicrocode in one or more ROM, RAM or PROM chips, field programmable gatearrays (FPGAs) or as an Application Specific Integrated Circuit (ASIC).The software or firmware or other such configurations can be installedonto the computerized device (e.g., during operating system execution orduring environment installation) to cause the computerized device toperform the techniques explained herein as embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features will be apparent from the followingdescription of particular embodiments disclosed herein, as illustratedin the accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

FIG. 1 is a context diagram of a computing environment suitable for usewith configurations herein;

FIG. 2 is a flowchart of a particular configuration in the environmentof FIG. 1;

FIG. 3 is a block diagram of a server in the environment of FIG. 1;

FIGS. 4A-4E are charts of driving parameter distribution using thedistribution model of FIG. 3;

FIGS. 5A-5C are a flowchart of scoring computation in the server of FIG.3; and

FIGS. 6A, 6B-1, and 6B-2 show Graphical User Interface (GUI) screensrendered by the server of FIGS. 1 and 3.

DETAILED DESCRIPTION

Configurations below depict a system and method for monitoring suspectevents indicative of distracted driving. Distracted driving is anyactivity that could divert a person's attention away from the primarytask of driving, particular interactive apps because text messaging,email usage and web browsing require visual, manual, and cognitiveattention from the driver, they are by far the most alarmingdistractions. This problem exists for all types of driver: professionalfleet drivers, non-commercial drivers who use a company vehicle forbusiness, drivers of their own private vehicles, inexperienced driversand teenaged drivers.

A method and system for detecting distracted driving associatesdistracted driving events to the road type, vehicle speed and vehicleacceleration (positive, negative and lateral) at the time of thedistracted driving event, and identifies severe distracted drivingevents from a large population of minor events using the Cauchydistribution equation, computing driver safety scores which relate timeof day, road type, vehicle speed, vehicle acceleration (positive,negative and lateral) and distracted driving using the Cauchydistribution equation, and providing summary and detail reports ofdriving scores and distracted driving events to concerned partiesincluding insurance companies, fleet managers, vehicle owners and theparents/guardians of teenaged drivers.

Several studies and authorities concur on the gravity of distracteddriving. The US Department of Transportation (USDOT) has launched anationwide campaign to stop distracted driving. Other statistics theysight are:

In 2009, 5,474 people were killed in crashes involving driverdistraction, and an estimated 448,000 were injured. (NHTSA).

16% of fatal crashes in 2009 involved reports of distracted driving.(NHTSA)

20% of injury crashes in 2009 involved reports of distracted driving.(NHTSA).

According to at least one source, teen drivers are more likely thanother age groups to be involved in a fatal crash where distraction isreported. In 2009, 16% of teen drivers involved in a fatal crash werereported to have been distracted. (NHTSA).

40% of all American teens say they have been in a car when the driverused a cell phone in a way that put people in danger. (Pew)

Drivers who use hand-held devices are 4 times more likely to get intocrashes serious enough to injure themselves (Monash University)

Text messaging creates a crash risk 23 times worse than driving whilenot distracted. (VTTI).

FIG. 1 is a context diagram of a computing environment suitable for usewith configurations herein. In FIG. 1, a monitored driving environment100, a driving evaluator application (app) 120 executes on a personalelectronic device 110 (personal device) such as a mobile phone orsmartphone. The app 120 gathers data and events pertaining to drivingperformance, and is associated with a driver 112 of a vehicle 116. Thedriver 112 is distinguished from a passenger 114 of the vehicle throughan electronic appliance that recognizes the driver's device 110 (mobilephone), such as a telematics box or other communication medium. A GPSinterface 126 access GPS technology 140 for location identification.

A cellphone antenna 130 or tower receives signals 121 from the personaldevice 110 for communicating suspect event data to a server 150 via apublic access network 132 such as the Internet. Messages 122 containingsuspect event data are sent to a server 150. The server 150 stores theevent data in a repository 152 for generating a report 124 of drivingactivity for display on a rendering device 126 such as a laptop, PC oranother mobile device. The rendering device 126 allows review of drivingperformance by a monitoring user 140, such as a parent, supervisor ormanager at a home 141 or office 142 location

FIG. 2 is a flowchart of a particular configuration in the environmentof FIG. 1. Referring to FIGS. 1 and 2, the method for tracking drivinghabits as disclosed herein includes, at step 200, associating a personaldevice 110 with a driver 112 in a vehicle 116, and determining that thedriver 112 is engaging in a suspect activity while driving, as depictedat step 201. Passenger presence is determined with a vehicle comparison,discussed further below, so that activities of the driver are noterroneously attributed to a passenger also having the app 120. The app120 invokes the server 150 to compare the suspect activity with aseverity scale indicative of a relative risk of the activity, as shownat step 202, and computes a score based on the comparison, as disclosedat step 203. The server 150 then reports the determined suspect activityand the score to the repository 152 for accumulating a driving historyof the driver 112, as depicted at step 204.

FIG. 3 is a block diagram of a server in the environment of FIG. 1.Referring to FIGS. 1 and 3, the application (app) 120 on the personaldevice 110 identifies suspect activities and sends a message 122 to theserver including the data concerning the suspect activity. The personaldevice 110 includes sensors and components for sensing the dataindicative of the suspect activity, such as a GPS (Global PositioningSystem) interface 130, accelerometer 132, telematics interface 134 andambient light sensor 136.

The GPS interface 130 employs GPS technology 140 for locating thepersonal device 110 using latitude and longitude components common toGPS measurements. The accelerometer 132 measures angular velocity fordetecting sharp turns and sudden acceleration and braking. Thetelematics interface 134 provides a link to a telematics appliance 135for identifying the vehicle, and can also be employed to offload the GPSburden, discussed further below. The ambient light sensor 138 candetermine day or night driving conditions, or alternatively the time ofday may be employed.

The server 150 includes a distribution model 154, scoring logic 156, andan event reporter 158. The distribution model 154 compares raw eventdata from a suspect event to previous events of the same drivingparameter, such as speed, to determine a statistical ranking of thesuspect event. The scoring logic computes a score for the suspect event,and aggregates the scores over time for computing an overall score of atime interval or driving period. The event reporter 158 stores andretrieves the event data, and renders reports 124 of computed scores aswell as individual events 122′ deemed to warrant a reporting urgency,discussed further below.

FIGS. 4A-4E show charts of driving parameter distribution using thedistribution model 154 of FIG. 3. The distribution model 154 is astatistical model such as a Cauchy distribution, although alternateconfigurations may employ standard deviation or other model. The modelis employed for associating unsafe driving events using a scalarparameter such as excessive speed, harsh acceleration, harsh braking,harsh cornering and distracted driving for comparison with scalar valuesof other drivers and computing a score indicative of the severity, ordeviation from norm, of the suspect event, and may include factors suchas the road type, time of day, traffic congestion, weather and otherambient conditions. FIG. 4A shows a graph 400 depicting in generalCauchy scoring model for speed (mph) 410 contrasted with a linear model412. A horizontal axis 404 indicates mph over a threshold (notnecessarily a posted speed limit); thresholds are user/supervisorsettable based on road type, with corresponding values 404′. A verticalaxis 402 has values 402′ and indicates a scoring impact for the speedvalues. Referring to FIGS. 4B and 4C, a point value per road type isshown to illustrate the effect on scoring for suspect events ondifferent road types. Each of road types 1, 2 and 3 are shownrespectively in columns 420, 421, 422 and 423. A road type refers to thegeneral speed and character of the road, such as width, volume andfrequency/sharpness of curves, and may be characterized as residential,rural, commercial, and highway, for example. The graph of FIG. 4C showscorresponding curves and axes 420′, 421′, 422′ and 423′ for respectivecolumns (values) 420, 421, 422 and 423. A corresponding speed thresholdvalue is also shown in table 425, which refers to the thresholdparameter in the Cauchy equation, discussed further below. As can beconstrued from the chart, road type 3 is likely the most restrictive incharacter, probably referring to a residential road where excessivespeed is strongly deterred, since a mere 5 mph over threshold speed hasa score impact from 100 to 37.5. Similarly, road type 1 may be ahighway, as the speed overages impact the score only mildly in the first5 mph over threshold.

FIGS. 4D and 4E illustrate changes to scoring criteria using the Cauchymodel. In FIGS. 4D and 4E, columns 451, 452 and 453 refer respectivelyto road types 1, 2 and 3, for speed overages 450. In contrast to FIGS.4B and 4C, however, type 3 roads are even less tolerant to a mere 5 mphoverage, reducing the score from 100 to 27.8, due to the differentthreshold parameters in table 475. Road types 1 and 2 are also morerestrictive to the 5 mph overage, at 86.7 and 81.8, respectively.Corresponding graphs 451′, 452′ and 453′ are shown for columns 451, 452and 453, respectively, against threshold axis 450′, reflecting column450.

The disclosed Cauchy Scoring employs road type, assigned speed andrecorded speed. Other parameters could be used. In the current methodsin use, billions of points and trips must be sorted to find events thatindicate risky driving patterns. This method allows easy and rapidsorting of driving events in low and high risk of crash categories.Additionally, the worst 10% of all drivers can be rapidly identified.

The characteristics of the Cauchy Distribution make it especially usefulin identifying marginal and extreme events from a large population ofminor events. The Cauchy Distribution modifies a standard bell curve(long tail) to a curve with short tails so that marginal and extremeevents can be easily identified.

As each sample is processed, the target parameter is compared to theacceptable threshold setting. Threshold settings can be in MPH or KPH.For example: A speed of 50 mph is compared to threshold of 40 mph forthat road type.

An acceleration event of +0.35 g is compared to a threshold of +0.275 g

A braking event of −0.40 g is compared to a threshold of −0.275 g

A cornering event of 0.30 g is compared to a threshold of 0.20 g

A distracted driving event at 30 mph is compared to a threshold of 0mph.

In the most often used method, a sample is taken and processed once persecond while the vehicle is in motion. This method has the beneficialeffect of normalizing the driving score over time as each point of atrip is scored independently. The total trip score is the sum of thescores for each point in the trip.

Note that the Cauchy Scoring can be tuned to score distracted drivingevents very harshly by adjusting the road type threshold setting. Asshown in the chart referenced below, the score for a sample point dropsquickly as the speed above the threshold increases.

The scoring process follows a Cauchy survival function of the speed of avehicle over a reference speed. The reference speed can be the roadspeed limit, the posted speed limit, the average road speed or any otherspeed threshold used for scoring purposes. The generalized scoringequation is as follows:

${{score}\left( {k,r} \right)} = {100*\left( {\frac{V}{2} - \frac{\arctan\left( {\left( {k - r - H} \right)/S} \right)}{\pi}} \right)}$The variables of the equation are:k: (measured) speed of the vehicler: reference speedIn another embodiment, the input variables can be positive, negative andlateral accelerations measured in g forces, meters per second persecond, or feet per second per second.The parameters can be defined as follows:V: vertical translation of the graphH: horizontal translation of the graphS: scale to define the graph slope

Scoring is affected not only by the speed of the vehicle but by someother variables like twilight, weather, age of the driver, type of car,geographic area, etc. So the parameters can be adjusted by conditionsnot related to speed. As a major effect on the score, besides the speed,the method employs different values of H depending of the time of theday using the nautical twilight. The nautical twilight is calculatedusing the time and the position of the vehicle to determine if thevehicle is being driven at day or night.

As practical examples, using miles per hour, we found the following goodparameter values (i.e. day H=13, night H=8) for scoring:

${{Day}\mspace{14mu}{{Score}\left( {k,r} \right)}} = {100*\left( {\frac{1.02}{2} - \frac{\arctan\left( {\left( {k - r - 13} \right)/1} \right)}{\pi}} \right)}$${{Night}\mspace{14mu}{{Score}\left( {k,r} \right)}} = {100*\left( {\frac{1.02}{2} - \frac{\arctan\left( {\left( {k - r - 8} \right)/1} \right)}{\pi}} \right)}$

FIGS. 5A-5C are a flowchart of scoring computation in the server of FIG.3. Referring to FIGS. 3 and 5A-5C, at step 300, the method for trackingdriving habits includes associating a personal device with a driver in avehicle. This may include associating the personal device with thevehicle by identifying a positioning device disposed in the vehicle of amonitored driver, in which the positioning device is a telematics unit(appliance) 135 responsive to the personal device 110, as depicted atstep 301. Telematics devices are commonly deployed with vehicles forproviding GPS support and communication with an interactive service foremergency or concierge types of response, depending on a desired servicelevel. Application 120 invocation may be facilitated by near fieldcommunication (NFC) based on proximity to the vehicle, such that theapplication is invoked automatically each time the driver enters thevehicle, as depicted at step 302.

In the event a user is not a driver, the server 150 concludes passengerstatus at step 303 by receiving a location indication of the personaldevice 110, as depicted at step 304, and receiving a location indicationof the positioning device 135, as shown at step 305. The server 150identifies a mismatch of the location indications as indicative ofpassenger 114 status of a user corresponding to the personal device 110,as depicted at step 306. Since the vehicle positioning device 135(telematics box) is not the one associated with the passenger's personaldevice, the server 150 concludes that the user is a passenger in anotherdriver's car. Alternatively, in the case of a corporation having shiftdrivers in the same vehicle, a time window is associated with thepersonal device of each user/employee to indicate which driver isassociated with the vehicle at a particular time.

The app 120 determines that the driver is engaging in a suspect activitywhile driving, as depicted at step 307, and takes one of severalactions, depending on the suspect event. At step 308, if the suspectactivity includes invoking applications on the personal device 110, theapp 110 compares the invoked application to a set of distractingapplications, as shown at step 308.

The suspect activity may also include at least one of speeding, sharpturning, sudden acceleration, sudden deceleration, and may be quantifiedusing a metric such as one of speed, angular velocity, linearacceleration. A measured driving parameter could then include speed andangular force measurable on a linear scale to driving parameters ofother drivers, as measured by an accelerometer and GPS sensors/messageson the personal device 110.

The personal device 110 compares the suspect activity with a severityscale indicative of a relative risk of the activity, typically viamessaging or an exchange with the server 150, as depicted at step 309.This includes determining a metric associated with the suspect activity,such as speed or a sudden change in direction, as depicted at step 310.A driving parameter associated with the determined metric is computedsuch as miles per hour (mph), angular velocity, or starting/stoppingtimes, as shown at step 311. The driving parameter is intended to be ascalar quantity that can be compared in a statistical manner (such asvia a Cauchy distribution) to suspect activities of other drivers. Thedriving parameter is compared to a statistical distribution of events ofother drivers for the same driving parameter, as depicted at step 312,and may be concluded as a high severity if the driving parameter isoutside a predetermined range of the statistical distribution, as shownat step 313. Such a high severity may be immediately reported to anauthority (monitoring user 140), while more benign suspect events arelogged and reported in summary form at a later time.

In the example configuration shown, in the event that the suspectactivity is speeding, at step 314, the driving parameter is vehiclespeed and the statistical distribution is a Cauchy distribution, asdepicted at step 314. In this case, determining the suspect activityincludes receiving a user selection of a road type and a threshold speedfor that road type, as shown at step 315. The road type, as discussedfurther below with respect to FIG. 5, generally denotes appropriatespeed due to such factors as width, sharp curves, visibility and density(rural, residential, etc.), for example, and is received by the app 120as an initialization or startup parameter. Other factors may be used.The app 120 determines that the vehicle is traveling on a particularroad type, due to GPS determined location and a road classificationdatabase indicative of the road type, as shown at step 316. The app 120may determine that the vehicle is exceeding the user (supervisor)defined threshold speed for the road type, which is independent from andmay be greater or less than the posted legal speed limit for the road,as shown at step 317.

The road type may be determined by any suitable manner, such as the roadclassification database discussed above, or other manner such as thespeed monitoring determination disclosed in the US patent applicationcited above. The road classification is distinguished from a postedspeed limit because road classification is defined by a differentauthority and is not necessarily determinable from the speed limit. GPSengine providers typically have their own road classification layerwhich provides a homogenous road classification system world wide, so aclass 2 road in the US would have the same speed profile as a class 2road in other countries. Other sources of road type include the USCensus Bureau Tiger Road Classification System.

In contrast, a posted speed limit is determined by local lawenforcement, and often changes over the same road type. For example,speed limits often vary when approaching a major intersection, while thegeneral road type would remain the same. The road type may be one ofrural, suburban, highway, city, commercial, residential and private, toname several. Other labels may be applied. The road type denotes astructure and character to a road, which is a factor in a level ofattentiveness and speed required for maintaining safe driving. A narrow,windy rural road requires more attention of the driver and lower speedto accommodate common and sharp turns which may come up suddenly. Acommercial street may be wide and straight, allowing a higher speed buthaving more congestion and vehicles likely to stop suddenly fordeliveries. A residential street commands a lower speed due to the riskof pedestrians. A highway has predictable curves and long visibility,and may be appropriate for higher speed and be more tolerant of alessened driver attention. Published road classifications identifyingsuch road types are obtainable.

For each of the suspect events discussed above, using the gatheredinformation, a score is computed based on the comparison (statisticalmodel of other suspect events), as depicted at step 318, and thedetermined suspect activity and the score reported to the repository 152for accumulating a driving history of the driver, as disclosed at step319.

From time to time during app execution, or at the bequest of theuser/supervisor 140, a conformation request signal may be sent from theserver to the app to confirm that the app is actively monitoring drivingso that the driver cannot avoid scrutiny by disabling or exiting the appor phone. At step 320, if a signal was received, then the server 150proceeds to confirm operation of the personal device at step 321 byreceiving a location indication of the personal device, as depicted atstep 322, and receives a location indication of the positioning device,as shown at step 323. The server 150 then compares the received locationindications to conclude driver presence in the vehicle and operation ofthe app 120 for monitoring driving, as shown at step 324.

Generally, a series of suspect events are gathered during an operatingsession or interval, and an aggregate score computed. Accordingly, atstep 325, a check is performed to identify if additional suspect eventsare being gathered, and accordingly the server 150 and app 120collectively determine a plurality of the suspect activities andcorresponding score and compute an aggregate score based on theplurality of reported suspect activities, and control reverts to step307 to monitor and gather the next suspect event, as shown at step 326.

FIGS. 6A, 6B-1, and 6B-2 show Graphical User Interface (GUI) screensrendered by the server of FIGS. 1 and 3. Referring to FIGS. 1, 3, arendering screen 126′ on the rendering device 126 presents the report124 of a monitored driver 112 for viewing by the monitoring user 140(parent, employer, etc.). Individual suspect events 122′ are shown in anevent window 184, and selection (highlighted event 185) displays anevent location icon 186 in a map display window 182 to identify thelocation at which the suspect event 122′ occurred. Statistical graphs ofdifferent event types (speed over threshold, hard stop, application use,etc.) are shown in statistics window 180, and show an individual drivergraph 174 next to a general distribution graph 172 based on the drivingpopulation at large (or for a specific suspect such as age, geography,etc.) as stored in the repository 152.

FIG. 6B shows a rendering of all events 122′ for a specific driver,sorted by category (driving, system, summary, etc.). An event labelcolumn 190 shows the specific event name, and whether it was posted onthe portal (column 192) or sent directly to contacts (monitoring user140) in column 194 for events having a significant severity or immediateneed for redress. The description column 196 clarifies the nature of theevent indicated in the label column 190.

Those skilled in the art should readily appreciate that the programs andmethods defined herein are deliverable to a computer processing andrendering device in many forms, including but not limited to a)information permanently stored on non-writeable storage media such asROM devices, b) information alterably stored on writeable non-transitorystorage media such as floppy disks, magnetic tapes, CDs, RAM devices,and other magnetic and optical media, or c) information conveyed to acomputer through communication media, as in an electronic network suchas the Internet or telephone modem lines. The operations and methods maybe implemented in a software executable object or as a set of encodedinstructions for execution by a processor responsive to theinstructions. Alternatively, the operations and methods disclosed hereinmay be embodied in whole or in part using hardware components, such asApplication Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGAs), state machines, controllers or other hardwarecomponents or devices, or a combination of hardware, software, andfirmware components.

While the system and methods defined herein have been particularly shownand described with references to embodiments thereof, it will beunderstood by those skilled in the art that various changes in form anddetails may be made therein without departing from the scope of theinvention encompassed by the appended claims.

What is claimed is:
 1. A method for tracking driving habits, comprising:receiving an indication of an association of a personal device with adriver in a vehicle, the personal device being a telecommunications andcomputing device associated with a driver of the vehicle; determiningthat the driver is engaging in a suspect activity while driving;comparing the suspect activity with a severity scale indicative of arelative risk of the activity, comparing further comprising: determininga metric associated with the suspect activity; computing a first drivingparameter associated with the determined metric; comparing the drivingparameter to a statistical distribution, including a Cauchy distributiontranslated by a second driving parameter, of events of other drivers forthe first driving parameter, the first driving parameter being a scalarquantity pertaining to vehicle motion and the second driving parameterbased on ambient conditions; and concluding a high severity if thedriving parameter is outside a predetermined range of the Cauchydistribution; computing a score based on the comparison; and reportingthe determined suspect activity and the score to a repository foraccumulating a renderable driving history of the driver indicative ofdistracted driving events.
 2. The method of claim 1 further comprisingdetermining a plurality of the suspect activities and correspondingscore and computing an aggregate score based on the plurality ofreported suspect activities.
 3. The method of claim 1 wherein thesuspect activity is excessive speed, the driving parameter is vehiclespeed.
 4. The method of claim 1 wherein the suspect activity includesinvoking applications on the personal device, and comparing the invokedapplication to a set of distracting applications.
 5. The method of claim1 wherein determining the suspect activity includes: receiving a userselection of a road type and a threshold speed for that road type;determining that the vehicle is traveling on the road type by referenceto a GPS location and external road classification database; anddetermining that the vehicle is exceeding the threshold speed for theroad type.
 6. The method of claim 1 further comprising associating thepersonal device with the vehicle by: identifying a positioning devicedisposed in the vehicle of a monitored driver, the positioning devicebeing a telematics unit responsive to the personal device.
 7. The methodof claim 1 wherein the suspect activity includes at least one ofspeeding, sharp turning, sudden acceleration, sudden deceleration, themetric includes one of speed, angular velocity, linear acceleration, andthe driving parameter includes speed and angular force measurable on alinear scale to driving parameters of other drivers.
 8. The method ofclaim 1 further comprising confirming operation of the personal deviceby: receiving a location indication of the personal device; receiving alocation indication of the positioning device; comparing the receivedlocation indications to conclude driver presence in the vehicle.
 9. Themethod of claim 8 further comprising concluding passenger status byreceiving a location indication of the personal device receiving alocation indication of the positioning device; identifying a mismatch ofthe location indications as indicative of passenger status of a usercorresponding to the personal device.
 10. The method of claim 1 furthercomprising invoking the association of the personal device via nearfield communication (NFC) based on proximity to the vehicle, theapplication invoked automatically each time the driver enters thevehicle.
 11. The method of claim 3 wherein determining a speed furthercomprises receiving a plurality of GPS signals over a time interval, theGPS signals received via a telematics appliance in the vehicle, suchthat a native GPS receiver on the personal device imposes no batterydrain.
 12. The method of claim 1 further comprising receiving a drivingparameter from the associated device, the driving parameter indicativeof the suspect activity; determining, based on the received drivingparameter, a metric adapted to rank the suspect activity associated withthe driving parameter; and comparing the computed score to scorescomputed for other drivers.
 13. The method of claim 1 further comprisingranking the score based on the scores of other drivers using a Cauchydistribution.
 14. The method of claim 1 further comprising receiving afirst stream of distracted driving parameters from the deviceidentifying a second stream of server side inputs, and invoking a Cauchydistribution for analyzing the first and second input streams.
 15. Themethod of claim 1 wherein computing further comprises invoking a Cauchydistribution for identifying a relative risk presented by the computedscore based on scores computed for other drivers.
 16. The method ofclaim 1 wherein the Cauchy distribution is based on a nautical twilightindicative of whether the vehicle is being driven at day or night.
 17. Amethod for tracking driving events, comprising: determining, via apersonal device associated with a driver in a vehicle, a speed of thevehicle based on a computed positioning of the personal device;receiving a user defined speed threshold corresponding to a road typedetermined from the position of the vehicle, the threshold defined forthe road type, the personal device being a telecommunications andcomputing device associated with a driver of the vehicle; receiving alocation indication of the personal device; receiving a locationindication of a positioning device; comparing the received locationindications to conclude driver presence in the vehicle; and comparing afirst driving parameter indicative of the speed to the receivedthreshold and reporting the determined speed and the road type if thedetermined speed exceeds the threshold corresponding to the road typecurrently being traveled for rendering a driving history of the driverindicative of distracted driving events, comparing further comprising:comparing the driving parameter to a statistical distribution, includinga Cauchy distribution translated by a second driving parameter, ofevents of other drivers for the first driving parameter, the firstdriving parameter being a scalar quantity pertaining to vehicle motionand the second driving parameter based on ambient conditions includingthe road type; concluding a high severity if the driving parameter isoutside a predetermined range of the Cauchy distribution; computing ascore based on the comparison; and reporting the determined suspectactivity and the score to a repository for accumulating a renderabledriving history of the driver indicative of distracted driving events.18. The method of claim 17 wherein determining the position furthercomprises receiving a positioning signal from a telematics appliance inthe vehicle, the telematics appliance receiving the positioning signalfrom a remote source, receiving the positioning signal from thetelematics appliance consuming less energy than receiving thepositioning signal from the remote source.
 19. The method of claim 17further comprising: receiving a user selection of a road type and athreshold speed for that road type; determining that the vehicle istraveling on the road type; and determining that the vehicle isexceeding the threshold speed for the road type.
 20. The method of claim17 wherein the driving parameters include speed, location, time of day,accelerometer g-force, fuel usage, device app usage, and devicekeystrokes.